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Bayesian Data Analysis

4.20  ·  Rating details ·  407 ratings  ·  15 reviews
Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis usi
Hardcover, 3rd Edition, 675 pages
Published November 7th 2013 by CRC Press (first published 1995)
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4.20  · 
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 ·  407 ratings  ·  15 reviews

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Hyokun Yun
Jun 18, 2015 rated it really liked it
My impression on this book from what people around told me before actually reading it was that this book is the canonical textbook for those who want to get into Bayesian statistics. After having read this book from cover to cover, however, I do not think it is a good idea to start learning Bayesian statistics with this book, as it covers very wide range of topics and therefore does not get into much technical depth for most of them. I think this book is ideal for someone like me who has very ba ...more
Jul 05, 2016 rated it really liked it  ·  review of another edition
The book is good in that it is rigorous, moves fairly quickly, and incorporates a lot of important stuff about robustness, computation, and so on. However, I also find that it moves a little too quickly over some of the derivations of formulas and is not sufficiently clear in its intended meaning in some places. Also the exercises at the end of the chapter do not seem to me to be answerable based on the information that occurs in the chapter. In short the information is extremely valuable but th ...more
Radovan Kavický
Jun 07, 2011 rated it really liked it  ·  review of another edition
Shelves: data
This book's biggest strength is its introduction of most of the important ideas in Bayesian statistics through well-chosen examples. These are examples are not contrived: many of them came up in research by the authors over the past several years. Most examples follow a logical progression that was probably used in the original research: a simple model is fit to data; then areas of model mis-fit are sought, and a revised model is used to address them. This brings up another strength of the book: ...more
Michael Culbertson
A canonical reference text. For a more friendly and accessible introduction, I'd recommend Statistical Rethinking: A Bayesian Course with Examples in R and Stan.
Terran M
Apr 25, 2018 rated it really liked it  ·  review of another edition
This is a challenging but rewarding book on Bayesian statistics. Before you get to any kind of computerized methods, you're going to have to get through a substantial amount of somewhat tersely presented calculus with conjugate priors; this level of rigor is both the strength and weakness of this presentation. If I could do it all again, I wouldn't read this book first - I'd read Kruschke, and then Gelman and Hill, and then come back to this book.

n.b. they're on the 3rd edition now
Bing Wang
May 15, 2018 rated it it was amazing  ·  review of another edition
Skipped the last chapter.

Very detailed bayesian tutorial. Part I, II and IV are more practical for everyday's work. Should combine this text book with some practical coding exercise together. Such as pymc3 in python.

Help to organize/connect/compare many knowledge knots like mixed effect model vs hierarchical model, kriging vs gaussian process.
May 08, 2017 rated it really liked it  ·  review of another edition
This is the textbook for my Bayesian Data Analysis book. This book contains lots of real data analysis examples, and some example are repeated several times through out the book, for example a 8-school SAT score example appears in both single-parameters models and in hierarchical models. I really like how this book teaches you how to solve real problems.
Mar 18, 2018 rated it really liked it
More of theory and general example. It would be great to have more coding coach.
Tobias Wolf
Sep 23, 2016 rated it really liked it  ·  review of another edition
In the end I'm a bit torn. Everybody describes it as the bible of bayesian statistics and it indeed covers a wide range of topics and by that supersedes all other general textbooks on bayesianism I know of. A great amount of literature is given after each chapter for everyone who wants to learn more about specific stuff. As a first time introduction on the other hand it's an incredible bad choice - if you don't know the basics you are going to have a bad time.
Jun 16, 2009 rated it liked it  ·  review of another edition
Shelves: statistics
Gelman has complained about sloppy notation in other books, which is weird because the notation here seems extra sloppy -- no bold or anything to differentiate between scalars and vectors, which can be a pain sometimes. But if you already have at least a tiny bit of exposure to basic Bayesian stats in the usual notation, this seems to be a good and accessible way to expand your knowledge.
May 28, 2013 rated it really liked it  ·  review of another edition
classic. must have. I have owned several editions.
the definitive textbook for bayesian statistics. almost everything is here, but it's definitely not a gentle introduction.
Ram Das
Oct 04, 2016 rated it it was amazing  ·  review of another edition
An excellent book for bayesian analysis
Jim N
Sep 23, 2007 rated it liked it  ·  review of another edition
challenging for a poor mathematician
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